In this study, Fast Fourier transform (FFT) and autoregressive (AR) methods were selected for processing the photoplethysmogram (PPG), electrocardiogram (ECG), electroencephalogram (EEG) signals recorded in order to examine the effects of pulsed electromagnetic field (PEMF) at extremely low frequency (ELF) upon the human electrophysiological signal behavior. The parameters in the autoregressive (AR) method were found by using the least squares method. The power spectra of the PPG, ECG, and EEG signals were obtained by using these spectral analysis techniques. These power spectra were then used to compare the applied methods in terms of their frequency resolution and the effects in extraction of the features representing the PPG, ECG, and EEG signals. Some conclusions were drawn concerning the efficiency of the FFT and least squares AR methods as feature extraction methods used for representing the signals under study.
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http://dx.doi.org/10.1007/s10916-007-9123-7 | DOI Listing |
Respir Physiol Neurobiol
January 2025
Key Laboratory of Biomedical Information Engineering of Education Ministry, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China. Electronic address:
The central neural mechanism plays an important role in cardiopulmonary coupling. How the brain stem affects the cardiopulmonary coupling is relatively clear, but there are few studies on the cerebral cortex activity of cardiopulmonary coupling. We aim to study the response of the cerebral cortex for cardiopulmonary phase synchronization enhancement.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, PR China; Shanghai Yangpu Mental Health Center, Shanghai, 200093, PR China. Electronic address:
Background And Objective: The hybrid brain computer interfaces (BCI) combining electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have attracted extensive attention for overcoming the decoding limitations of the single-modality BCI. With the deepening application of deep learning approaches in BCI systems, its significant performance improvement has become apparent. However, the scarcity of brain signal data limits the performance of deep learning models.
View Article and Find Full Text PDFMed J Malaysia
January 2025
National University of Malaysia, Faculty of Medicine, Department of Medicine, Kuala Lumpur, Malaysia.
Introduction: Stroke is a major cause of morbidity and mortality worldwide. While electroencephalography (EEG) offers valuable data on post-stroke brain activity, qualitative EEG assessments may be misinterpreted. Therefore, we examined the potential of quantitative EEG (qEEG) to identify key band frequencies that could serve as potential electrophysiological biomarkers in stroke patients.
View Article and Find Full Text PDFMol Psychiatry
January 2025
Siena Brain Investigation and Neuromodulation Lab, Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy.
Ketamine, a dissociative compound, shows promise in treating mood disorders, including treatment-resistant depression (TRD) and bipolar disorder (BD). Despite its therapeutic potential, the neurophysiological mechanisms underlying ketamine's effects are not fully understood. This study explored acute neurophysiological changes induced by subanesthetic doses of ketamine in BD patients with depression using electroencephalography (EEG) biomarkers.
View Article and Find Full Text PDFNeural Netw
January 2025
Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region; Department of Computing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region. Electronic address:
In this work, we propose a Fine-grained Hemispheric Asymmetry Network (FG-HANet), an end-to-end deep learning model that leverages hemispheric asymmetry features within 2-Hz narrow frequency bands for accurate and interpretable emotion classification over raw EEG data. In particular, the FG-HANet extracts features not only from original inputs but also from their mirrored versions, and applies Finite Impulse Response (FIR) filters at a granularity as fine as 2-Hz to acquire fine-grained spectral information. Furthermore, to guarantee sufficient attention to hemispheric asymmetry features, we tailor a three-stage training pipeline for the FG-HANet to further boost its performance.
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